A so-called pathological diagnosis has been actively performed in which a tissue section sampled from a biological object of human beings, animals, or the like is observed by a microscope, and the presence or absence of a pathological change, the type of pathological change, or the like is diagnosed. In such a pathological diagnosis, it is general that the tissue section, which is a diagnosis target, is subjected to treatments such as fixing, embedding, slicing, and staining in this order, and then, is provided to the observation using the microscope.
The observation using the microscope, for example, includes bright field observation, fluorescent observation, and the like, and a bright field image can be acquired by imaging in the bright field observation, and a fluorescent image can be acquired by imaging in the fluorescent observation. The bright field image, for example, includes an image capturing a plurality of cell morphologies in the tissue section stained with a predetermined staining reagent (also referred to as a cell morphology image). In addition, the fluorescent image, for example, includes an image capturing the emission of a fluorescent substance contained in particles which are bonded to a specific substance (also referred to as a light emitting state image).
Further, in the cell morphology image, an image where a distribution of an area in which a specific portion of a cell is captured (also referred to as a cell area) is represented by a display element (also referred to as a cell distribution image) can be acquired, and an image in which a distribution of the specific substance is represented by the display element (also referred to as a substance distribution image) can be acquired from the light emitting state image. Here, a case is considered in which the specific substance, for example, is a specific protein in a cancer tissue, and the specific portion is a cell nucleus.
In this case, a feature amount quantitatively representing an expression state of the specific substance in the tissue section can be calculated from the cell distribution image and the substance distribution image. For example, a feature amount such as the existing number of specific substances per one cell nucleus and the existing number of specific substances per unit area of the cell nucleus can be calculated from a relationship between a distribution of a fluorescent bright point in the substance distribution image and a distribution of a cell area in the cell distribution image. By using such a feature amount, it is possible to perform a pathological diagnosis with respect to various cancers or the like. At this time, for example, the feature amount can be used in the form of a histogram or the like in which a plurality of values are sorted into blocks as a class, and a frequency for each block is represented by a bar graph. Accordingly, a clue at the time of specifically determining a degree of malignancy or the like of the cancer of a patient can is obtained.
Here, in order to accurately perform such a pathological diagnosis, it is necessary to acquire a cell distribution image accurately reflecting an actual state of the tissue section.
Here, the light emitting state image is an image which captures a state where a fluorescent substance emits light having a specific wavelength to the dark background and has a comparatively high contrast. For this reason, for example, extraction and binarization processing of a color component according to a specific wavelength by using the light emitting state image as a target are sequentially performed, and thus, the distribution of the specific substance can be accurately detected. That is, a substance distribution image accurately reflecting the actual state of the tissue section can be comparatively easily acquired. On the other hand, the cell morphology image displays a tendency in which the contrast is obviously low, compared to the light emitting state image. For this reason, various image processings for obtaining a cell distribution image from the cell morphology image have been proposed (for example, Patent Literatures 1 and 2).
For example, in Patent Literature 1, a pixel group, which is a candidate of a cell center, is extracted from the cell morphology image, only a pixel suitable as the cell center in the pixel group is selected according to a predetermined criteria, and a pixel forming the outline of the cell is selected from positional information of the selected cell center pixel and a direction of a concentration gradient of the peripheral pixel. In addition, in Patent Literature 2, a cell group having the characteristics similar to those of a specific cell of interest which is designated by a user from a plurality of cells in the cell morphology image subjected to predetermined processing is automatically extracted.